Graph self-supervised learning (GSSL), which is capable of learning representations of graph structured data without artificially crafted labels, has numerous application scenarios in real systems [1]}.
Recently, graph contrastive learning (GCL) [2]}, [3]}, [4]} has made a big splash on GSSL, successfully applying contrastive learning from the field of computer vision to graph data.
By pulling the relevant pairs (positive) closer and pushing the irrelevant pairs (negative) farther on the latent space, GCL methods achieve results comparable to, and sometimes surpassing, supervised methods.
